code: def test_batch_separate(self):
      a = torch.tensor([[4, 2, 8], [1, 2, 3]], dtype=torch.float).view(
          2, 3, 1
      )
      b = torch.tensor([[0, 2], [-1, 2]], dtype=torch.float).view(
          2, 2, 1
      )
      period = torch.tensor([1, 2], dtype=torch.float).view(2, 1, 1)
      lengthscale = torch.tensor([2, 1], dtype=torch.float).view(
          2, 1, 1
      )
      kernel = PeriodicKernel(batch_shape=torch.Size([2])).initialize(
          lengthscale=lengthscale, period_length=period
      )
      kernel.eval()
  
      actual = torch.zeros(2, 3, 2)
      for k in range(2):
          diff = a[k].unsqueeze(1) - b[k].unsqueeze(0)
          diff = diff * math.pi / period[k].unsqueeze(-1)
          actual[k] = (
              diff.sin()
              .pow(2)
              .sum(dim=-1)
              .div(lengthscale[k])
              .mul(-2.0)
              .exp_()
          )
  
      res = kernel(a, b).to_dense()
      self.assertLess(torch.norm(res - actual), 1e-5)
  
file_path: gpytorch/test/kernels/test_periodic_kernel.py
imports: import math
  import unittest
  import torch
  from gpytorch.kernels import PeriodicKernel
  from gpytorch.priors import NormalPrior
  from gpytorch.test.base_kernel_test_case import BaseKernelTestCase
label_desc: Use image node_id 7 for calling the TestPeriodicKernel obj's underlying member method code with example usage: obj.test_batch_separate() without return types